Query engines & federation · Apache Software Foundation

Apache Spark

Open-source distributed processing engine for large-scale batch, SQL, streaming and machine-learning workloads.

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Apache Spark is an open-source distributed computing engine for processing large datasets across a cluster, combining batch processing, SQL queries (Spark SQL), micro-batch/streaming (Structured Streaming), and machine learning (MLlib) under one unified API available in Python, Scala, Java and R. Its in-memory execution model made it substantially faster than earlier MapReduce-based tools for iterative workloads such as ETL pipelines and model training. Spark runs on its own standalone cluster manager, on Kubernetes, or on YARN, and reads from and writes to virtually any storage layer including S3, HDFS, Iceberg and Delta Lake. It is self-hosted by default; the commercial company Databricks, founded by Spark's original creators, offers it as a managed lakehouse platform with additional tooling. Spark is typically the default choice for large-scale data engineering and ML pipelines rather than for low-latency interactive dashboards.

At a glance

Vendor Apache Software Foundation
Pricing model Open source + paid options
Free tier Yes
Deployment Self-hosted
Open source Yes (Apache-2.0)
Best for Data engineering teams building large-scale batch ETL, streaming or machine-learning pipelines.

Pricing

Free and open source under the Apache Software Foundation; managed lakehouse platforms built on Spark, such as Databricks, are priced separately.

Pricing has not been verified yet — see the vendor's site.

Features

  • Unified batch, SQL, streaming and ML APIs
  • In-memory distributed execution (RDDs/DataFrames)
  • Structured Streaming for near-real-time pipelines
  • MLlib for distributed machine learning
  • Runs on Kubernetes, YARN or standalone
  • Broad connector ecosystem for storage and lakehouse formats

Integrations

Profile last reviewed September 21, 2026

Head to head

Alternatives

Apache Flink Open-source stream-processing engine for stateful, low-latency event processing at scale, with batch support. Trino Open-source distributed SQL engine that queries data across warehouses, lakes and databases without moving it. Ray Open-source Python framework for distributing machine-learning training, tuning and general compute across a cluster. Dask Open-source Python library that parallelizes NumPy, pandas and scikit-learn workflows across cores or a cluster.

Apache Spark in the index now

Terms to know

Data pipeline The automated sequence of steps that moves data from source systems to a destination, transforming it along the way. ETL (extract, transform, load) A data integration pattern that transforms data before loading it, unlike ELT which transforms after loading. Machine learning Building models that learn patterns from data to make predictions or decisions, rather than following hand-coded rules. Apache Spark An open-source distributed computing engine for processing large datasets in parallel across a cluster of machines. Batch processing Processing data in large, scheduled groups rather than continuously as each record arrives. Distributed computing Splitting a computation across multiple machines that coordinate over a network to solve it faster or at larger scale.

Related guides

How to choose a distributed query engine This category bundles two different jobs — federating SQL across existing systems, and distributed compute for pipelines and ML — start by telling them apart.